AIMC Topic: Sepsis

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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations.

Scientific reports
Sepsis is a condition resulting from the uncontrolled immune response to infection, leading to widespread inflammatory damage and potentially fatal organ dysfunction. Currently, there is a lack of specific prevention and treatment strategies for seps...

Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.

Critical care (London, England)
Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and perso...

Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in the influence of short-chain fatty acids on the development of sepsis.

Scientific reports
Sepsis is a systemic inflammatory response syndrome caused by infection, which has a high morbidity and mortality. Short-chain fatty acids (SCFAs) have been proved to improve the outcome of sepsis by regulating immunity and metabolism, but its specif...

Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure.

Hereditas
BACKGROUND: Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response....

In-depth bioinformatics analysis uncovers the crosstalk genes and immune interactions among diagnostic markers linked to natural killer cells in patients with cirrhosis and sepsis.

Clinical and experimental medicine
Patients with cirrhosis face an elevated risk of developing sepsis, leading to an escalating mortality rate. This study focuses on the link between natural killer (NK) cells, cirrhosis, and sepsis. Our goal is to identify NK cell-related genes that c...

Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.

BMC geriatrics
BACKGROUND: Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine ...

Integrative network pharmacology and multi-omics reveal anisodamine hydrobromide's multi-target mechanisms in sepsis.

Scientific reports
Sepsis, marked by hyperinflammation and subsequent immunosuppression, lacks effective phase-specific therapies. Although anisodamine hydrobromide (Ani HBr) reduced 28-day mortality in our prior trial, its mechanisms remained unclear. Here, we integra...

Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

Physiological measurement
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...

Calcium ion dynamic trajectory is associated with prognosis in patients with sepsis: A potential class mixture modeling study.

International immunopharmacology
Disturbed calcium homeostasis in patients with sepsis is associated with poor prognosis; however, its dynamic pattern and clinical significance remain unclear. This study aimed to establish an early warning system for calcium homeostasis in sepsis by...

Early detection of ICU-acquired infections using high-frequency electronic health record data.

BMC medical informatics and decision making
BACKGROUND: Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leve...